Introducing a Navigation Algorithm for Reducing the Spread of Diseases in Public Transport Networks

Area of Science:

• Urban Planning
• Transportation Engineering
• Public Health

Background:

• Crowded public transport bottlenecks pose urban planning challenges.
• The SARS-CoV-2 pandemic heightened the need for social distancing in transit.
• Effective passenger flow management is crucial for public health and network efficiency.

Purpose of the Study:

• To develop a dynamic navigation approach for distributing passenger load across urban public transport networks.
• To enable passengers to avoid dangerously crowded stations through modified routing requests.
• To assess the impact of avoiding key transit nodes on overall travel time.

Main Methods:

• Introduction of new constraints into routing algorithms to allow node avoidance.
• Integration of multiple transport modes including walking, cycling, and public transit (metro, bus, tram).
• Simulation of the approach using real-world data from Munich's public transport network in cooperation with MVG.

Main Results:

• The proposed approach successfully reroutes passengers to avoid crowded stations.
• Simulations in Munich showed a minor increase in average travel time from 26.5 to 26.8 minutes when avoiding major hubs.
• The navigation system effectively distributes passenger load across the network.

Conclusions:

• Dynamic passenger navigation can mitigate overcrowding in public transport.
• The slight increase in travel time is a justifiable cost for enhanced passenger safety and reduced transmission risk.
• This approach offers a viable strategy for safer urban mobility in public health crises.